/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    LogisticBase.java
 *    Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.functions;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.SimpleLinearRegression;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;

/**
 * Base/helper class for building logistic regression models with the LogitBoost algorithm.
 * Used for building logistic model trees (weka.classifiers.trees.lmt.LMT)
 * and standalone logistic regression (weka.classifiers.functions.SimpleLogistic).
 *
 <!-- options-start -->
 * Valid options are: <p/>
 *
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 *
 <!-- options-end -->
 *
 * @author Niels Landwehr
 * @author Marc Sumner
 * @version $Revision: 1.9 $
 */
public class NewLogisticBase
    extends Classifier
    implements WeightedInstancesHandler {

    /** for serialization */
    static final long serialVersionUID = 168765678097825064L;

    /** Header-only version of the numeric version of the training data*/
    protected Instances m_numericDataHeader;
    /**
     * Numeric version of the training data. Original class is replaced by a numeric pseudo-class.
     */
    protected Instances m_numericData;

    /** Training data */
    protected Instances m_train;

    /** Use cross-validation to determine best number of LogitBoost iterations ?*/
    protected boolean m_useCrossValidation;

    /**Use error on probabilities for stopping criterion of LogitBoost? */
    protected boolean m_errorOnProbabilities;

    /**Use fixed number of iterations for LogitBoost? (if negative, cross-validate number of iterations)*/
    protected int m_fixedNumIterations;

    /**Use heuristic to stop performing LogitBoost iterations earlier?
     * If enabled, LogitBoost is stopped if the current (local) minimum of the error on a test set as
     * a function of the number of iterations has not changed for m_heuristicStop iterations.
     */
    protected int m_heuristicStop = 50;

    /**The number of LogitBoost iterations performed.*/
    protected int m_numRegressions = 0;

    /**The maximum number of LogitBoost iterations*/
    protected int m_maxIterations;

    /**The number of different classes*/
    protected int m_numClasses;

    /**Array holding the simple regression functions fit by LogitBoost*/
    protected SimpleLinearRegression[][] m_regressions;

    /**Number of folds for cross-validating number of LogitBoost iterations*/
    protected static int m_numFoldsBoosting = 5;

    /**Threshold on the Z-value for LogitBoost*/
    protected static final double Z_MAX = 3;

    /** If true, the AIC is used to choose the best iteration*/
    private boolean m_useAIC = false;

    /** Effective number of parameters used for AIC / BIC automatic stopping */
    protected double m_numParameters = 0;

    /**Threshold for trimming weights. Instances with a weight lower than this (as a percentage
     * of total weights) are not included in the regression fit.
     **/
    protected double m_weightTrimBeta = 0;

    /**
     * Constructor that creates LogisticBase object with standard options.
     */
    public NewLogisticBase(){
	m_fixedNumIterations = -1;
	m_useCrossValidation = true;
	m_errorOnProbabilities = false;
	m_maxIterations = 500;
        m_useAIC = false;
        m_numParameters = 0;
    }

    /**
     * Constructor to create LogisticBase object.
     * @param numBoostingIterations fixed number of iterations for LogitBoost (if negative, use cross-validation or
     * stopping criterion on the training data).
     * @param useCrossValidation cross-validate number of LogitBoost iterations (if false, use stopping
     * criterion on the training data).
     * @param errorOnProbabilities if true, use error on probabilities
     * instead of misclassification for stopping criterion of LogitBoost
     */
    public NewLogisticBase(int numBoostingIterations, boolean useCrossValidation, boolean errorOnProbabilities){
	m_fixedNumIterations = numBoostingIterations;
	m_useCrossValidation = useCrossValidation;
	m_errorOnProbabilities = errorOnProbabilities;
	m_maxIterations = 500;
        m_useAIC = false;
        m_numParameters = 0;
    }

    /**
     * Builds the logistic regression model usiing LogitBoost.
     *
     * @param data the training data
     * @throws Exception if something goes wrong
     */
    public void buildClassifier(Instances data) throws Exception {

	m_train = new Instances(data);

	m_numClasses = m_train.numClasses();

	//init the array of simple regression functions
	m_regressions = initRegressions();
	m_numRegressions = 0;

	//get numeric version of the training data (class variable replaced  by numeric pseudo-class)
	m_numericData = getNumericData(m_train);

	//save header info
	m_numericDataHeader = new Instances(m_numericData, 0);


	if (m_fixedNumIterations > 0) {
	    //run LogitBoost for fixed number of iterations
	    performBoosting(m_fixedNumIterations);
	} else if (m_useAIC) { // Marc had this after the test for m_useCrossValidation. Changed by Eibe.
            //run LogitBoost using information criterion for stopping
            performBoostingInfCriterion();
        } else if (m_useCrossValidation) {
	    //cross-validate number of LogitBoost iterations
	    performBoostingCV();
	} else {
	    //run LogitBoost with number of iterations that minimizes error on the training set
	    performBoosting();
	}

	//only keep the simple regression functions that correspond to the selected number of LogitBoost iterations
	m_regressions = selectRegressions(m_regressions);
    }

    /**
     * Runs LogitBoost, determining the best number of iterations by cross-validation.
     *
     * @throws Exception if something goes wrong
     */
    protected void performBoostingCV() throws Exception{

	//completed iteration keeps track of the number of iterations that have been
	//performed in every fold (some might stop earlier than others).
	//Best iteration is selected only from these.
	int completedIterations = m_maxIterations;

	Instances allData = new Instances(m_train);

	allData.stratify(m_numFoldsBoosting);

	double[] error = new double[m_maxIterations + 1];

	for (int i = 0; i < m_numFoldsBoosting; i++) {
	    //split into training/test data in fold
	    Instances train = allData.trainCV(m_numFoldsBoosting,i);
	    Instances test = allData.testCV(m_numFoldsBoosting,i);

	    //initialize LogitBoost
	    m_numRegressions = 0;
	    m_regressions = initRegressions();

	    //run LogitBoost iterations
	    int iterations = performBoosting(train,test,error,completedIterations);
	    if (iterations < completedIterations) completedIterations = iterations;
	}

	//determine iteration with minimum error over the folds
	int bestIteration = getBestIteration(error,completedIterations);

	//rebuild model on all of the training data
	m_numRegressions = 0;
	performBoosting(bestIteration);
    }

    /**
     * Runs LogitBoost, determining the best number of iterations by an information criterion (currently AIC).
     */
    protected void performBoostingInfCriterion() throws Exception{

        double criterion = 0.0;
        double bestCriterion = Double.MAX_VALUE;
        int bestIteration = 0;
        int noMin = 0;

        // Variable to keep track of criterion values (AIC)
        double criterionValue = Double.MAX_VALUE;

        // initialize Ys/Fs/ps
        double[][] trainYs = getYs(m_train);
        double[][] trainFs = getFs(m_numericData);
        double[][] probs = getProbs(trainFs);

        // Array with true/false if the attribute is included in the model or not
        boolean[][] attributes = new boolean[m_numClasses][m_numericDataHeader.numAttributes()];

        int iteration = 0;
        while (iteration < m_maxIterations) {

            //perform single LogitBoost iteration
            boolean foundAttribute = performIteration(iteration, trainYs, trainFs, probs, m_numericData);
            if (foundAttribute) {
                iteration++;
                m_numRegressions = iteration;
            } else {
                //could not fit simple linear regression: stop LogitBoost
                break;
            }

            double numberOfAttributes = m_numParameters + iteration;

            // Fill criterion array values
            criterionValue = 2.0 * negativeLogLikelihood(trainYs, probs) +
              2.0 * numberOfAttributes;

            //heuristic: stop LogitBoost if the current minimum has not changed for <m_heuristicStop> iterations
            if (noMin > m_heuristicStop) break;
            if (criterionValue < bestCriterion) {
                bestCriterion = criterionValue;
                bestIteration = iteration;
                noMin = 0;
            } else {
                noMin++;
            }
        }

        m_numRegressions = 0;
        performBoosting(bestIteration);
    }

    /**
     * Runs LogitBoost on a training set and monitors the error on a test set.
     * Used for running one fold when cross-validating the number of LogitBoost iterations.
     * @param train the training set
     * @param test the test set
     * @param error array to hold the logged error values
     * @param maxIterations the maximum number of LogitBoost iterations to run
     * @return the number of completed LogitBoost iterations (can be smaller than maxIterations
     * if the heuristic for early stopping is active or there is a problem while fitting the regressions
     * in LogitBoost).
     * @throws Exception if something goes wrong
     */
    protected int performBoosting(Instances train, Instances test,
				  double[] error, int maxIterations) throws Exception{

	//get numeric version of the (sub)set of training instances
	Instances numericTrain = getNumericData(train);

	//initialize Ys/Fs/ps
	double[][] trainYs = getYs(train);
	double[][] trainFs = getFs(numericTrain);
	double[][] probs = getProbs(trainFs);

	int iteration = 0;

 	int noMin = 0;
	double lastMin = Double.MAX_VALUE;

	if (m_errorOnProbabilities) error[0] += getMeanAbsoluteError(test);
	else error[0] += getErrorRate(test);

	while (iteration < maxIterations) {

	    //perform single LogitBoost iteration
	    boolean foundAttribute = performIteration(iteration, trainYs, trainFs, probs, numericTrain);
	    if (foundAttribute) {
		iteration++;
		m_numRegressions = iteration;
	    } else {
		//could not fit simple linear regression: stop LogitBoost
		break;
	    }

	    if (m_errorOnProbabilities) error[iteration] += getMeanAbsoluteError(test);
	    else error[iteration] += getErrorRate(test);

	    //heuristic: stop LogitBoost if the current minimum has not changed for <m_heuristicStop> iterations
	    if (noMin > m_heuristicStop) break;
	    if (error[iteration] < lastMin) {
		lastMin = error[iteration];
		noMin = 0;
	    } else {
		noMin++;
	    }
	}

	return iteration;
    }

    /**
     * Runs LogitBoost with a fixed number of iterations.
     * @param numIterations the number of iterations to run
     * @throws Exception if something goes wrong
     */
    protected void performBoosting(int numIterations) throws Exception{

	//initialize Ys/Fs/ps
	double[][] trainYs = getYs(m_train);
	double[][] trainFs = getFs(m_numericData);
	double[][] probs = getProbs(trainFs);

	int iteration = 0;

	//run iterations
	while (iteration < numIterations) {
	    boolean foundAttribute = performIteration(iteration, trainYs, trainFs, probs, m_numericData);
	    if (foundAttribute) iteration++;
	    else break;
	}

	m_numRegressions = iteration;
    }

    /**
     * Runs LogitBoost using the stopping criterion on the training set.
     * The number of iterations is used that gives the lowest error on the training set, either misclassification
     * or error on probabilities (depending on the errorOnProbabilities option).
     * @throws Exception if something goes wrong
     */
    protected void performBoosting() throws Exception{

	//initialize Ys/Fs/ps
	double[][] trainYs = getYs(m_train);
	double[][] trainFs = getFs(m_numericData);
	double[][] probs = getProbs(trainFs);

	int iteration = 0;

	double[] trainErrors = new double[m_maxIterations+1];
	trainErrors[0] = getErrorRate(m_train);

	int noMin = 0;
	double lastMin = Double.MAX_VALUE;

	while (iteration < m_maxIterations) {
	    boolean foundAttribute = performIteration(iteration, trainYs, trainFs, probs, m_numericData);
	    if (foundAttribute) {
		iteration++;
		m_numRegressions = iteration;
	    } else {
		//could not fit simple regression
		break;
	    }

	    trainErrors[iteration] = getErrorRate(m_train);

	    //heuristic: stop LogitBoost if the current minimum has not changed for <m_heuristicStop> iterations
	    if (noMin > m_heuristicStop) break;
	    if (trainErrors[iteration] < lastMin) {
		lastMin = trainErrors[iteration];
		noMin = 0;
	    } else {
		noMin++;
	    }
	}

	//find iteration with best error
        m_numRegressions = getBestIteration(trainErrors, iteration);
    }

    /**
     * Returns the misclassification error of the current model on a set of instances.
     * @param data the set of instances
     * @return the error rate
     * @throws Exception if something goes wrong
     */
    protected double getErrorRate(Instances data) throws Exception {
	Evaluation eval = new Evaluation(data);
	eval.evaluateModel(this,data);
	return eval.errorRate();
    }

    /**
     * Returns the error of the probability estimates for the current model on a set of instances.
     * @param data the set of instances
     * @return the error
     * @throws Exception if something goes wrong
     */
    protected double getMeanAbsoluteError(Instances data) throws Exception {
	Evaluation eval = new Evaluation(data);
	eval.evaluateModel(this,data);
	return eval.meanAbsoluteError();
    }

    /**
     * Helper function to find the minimum in an array of error values.
     *
     * @param errors an array containing errors
     * @param maxIteration the maximum of iterations
     * @return the minimum
     */
    protected int getBestIteration(double[] errors, int maxIteration) {
	double bestError = errors[0];
	int bestIteration = 0;
	for (int i = 1; i <= maxIteration; i++) {
	    if (errors[i] < bestError) {
		bestError = errors[i];
		bestIteration = i;
	    }
	}
	return bestIteration;
    }

    /**
     * Performs a single iteration of LogitBoost, and updates the model accordingly.
     * A simple regression function is fit to the response and added to the m_regressions array.
     * @param iteration the current iteration
     * @param trainYs the y-values (see description of LogitBoost) for the model trained so far
     * @param trainFs the F-values (see description of LogitBoost) for the model trained so far
     * @param probs the p-values (see description of LogitBoost) for the model trained so far
     * @param trainNumeric numeric version of the training data
     * @return returns true if iteration performed successfully, false if no simple regression function
     * could be fitted.
     * @throws Exception if something goes wrong
     */
    protected boolean performIteration(int iteration,
				       double[][] trainYs,
				       double[][] trainFs,
				       double[][] probs,
				       Instances trainNumeric) throws Exception {

	for (int j = 0; j < m_numClasses; j++) {
            // Keep track of sum of weights
            double[] weights = new double[trainNumeric.numInstances()];
            double weightSum = 0.0;

	    //make copy of data (need to save the weights)
	    Instances boostData = new Instances(trainNumeric);

	    for (int i = 0; i < trainNumeric.numInstances(); i++) {

		//compute response and weight
		double p = probs[i][j];
		double actual = trainYs[i][j];
		double z = getZ(actual, p);
		double w = (actual - p) / z;

		//set values for instance
		Instance current = boostData.instance(i);
		current.setValue(boostData.classIndex(), z);
		current.setWeight(current.weight() * w);

                weights[i] = current.weight();
                weightSum += current.weight();
	    }

            Instances instancesCopy = new Instances(boostData);

            if (weightSum > 0) {
                // Only the (1-beta)th quantile of instances are sent to the base classifier
                if (m_weightTrimBeta > 0) {
                    double weightPercentage = 0.0;
                    int[] weightsOrder = new int[trainNumeric.numInstances()];
                    weightsOrder = Utils.sort(weights);
                    instancesCopy.delete();


                    for (int i = weightsOrder.length-1; (i >= 0) && (weightPercentage < (1-m_weightTrimBeta)); i--) {
                        instancesCopy.add(boostData.instance(weightsOrder[i]));
                        weightPercentage += (weights[weightsOrder[i]] / weightSum);

                    }
                }

                //Scale the weights
                weightSum = instancesCopy.sumOfWeights();
                for (int i = 0; i < instancesCopy.numInstances(); i++) {
                    Instance current = instancesCopy.instance(i);
                    current.setWeight(current.weight() * (double)instancesCopy.numInstances() / weightSum);
                }
            }

	    //fit simple regression function
	    m_regressions[j][iteration].buildClassifier(instancesCopy);

	    boolean foundAttribute = m_regressions[j][iteration].foundUsefulAttribute();
	    if (!foundAttribute) {
		//could not fit simple regression function
		return false;
	    }

	}

	// Evaluate / increment trainFs from the classifier
	for (int i = 0; i < trainFs.length; i++) {
	    double [] pred = new double [m_numClasses];
	    double predSum = 0;
	    for (int j = 0; j < m_numClasses; j++) {
		pred[j] = m_regressions[j][iteration]
		    .classifyInstance(trainNumeric.instance(i));
		predSum += pred[j];
	    }
	    predSum /= m_numClasses;
	    for (int j = 0; j < m_numClasses; j++) {
		trainFs[i][j] += (pred[j] - predSum) * (m_numClasses - 1)
		    / m_numClasses;
	    }
	}

	// Compute the current probability estimates
	for (int i = 0; i < trainYs.length; i++) {
	    probs[i] = probs(trainFs[i]);
	}
	return true;
    }

    /**
     * Helper function to initialize m_regressions.
     *
     * @return the generated classifiers
     */
    protected SimpleLinearRegression[][] initRegressions(){
	SimpleLinearRegression[][] classifiers =
	    new SimpleLinearRegression[m_numClasses][m_maxIterations];
	for (int j = 0; j < m_numClasses; j++) {
	    for (int i = 0; i < m_maxIterations; i++) {
		classifiers[j][i] = new SimpleLinearRegression();
		classifiers[j][i].setSuppressErrorMessage(true);
	    }
	}
	return classifiers;
    }

    /**
     * Converts training data to numeric version. The class variable is replaced by a pseudo-class
     * used by LogitBoost.
     *
     * @param data the data to convert
     * @return the converted data
     * @throws Exception if something goes wrong
     */
    protected Instances getNumericData(Instances data) throws Exception{
	Instances numericData = new Instances(data);

	int classIndex = numericData.classIndex();
	numericData.setClassIndex(-1);
	numericData.deleteAttributeAt(classIndex);
	numericData.insertAttributeAt(new Attribute("'pseudo class'"), classIndex);
	numericData.setClassIndex(classIndex);
	return numericData;
    }

    /**
     * Helper function for cutting back m_regressions to the set of classifiers
     * (corresponsing to the number of LogitBoost iterations) that gave the
     * smallest error.
     *
     * @param classifiers the original set of classifiers
     * @return the cut back set of classifiers
     */
    protected SimpleLinearRegression[][] selectRegressions(SimpleLinearRegression[][] classifiers){
	SimpleLinearRegression[][] goodClassifiers =
	    new SimpleLinearRegression[m_numClasses][m_numRegressions];

	for (int j = 0; j < m_numClasses; j++) {
	    for (int i = 0; i < m_numRegressions; i++) {
		goodClassifiers[j][i] = classifiers[j][i];
	    }
	}
	return goodClassifiers;
    }

    /**
     * Computes the LogitBoost response variable from y/p values
     * (actual/estimated class probabilities).
     *
     * @param actual the actual class probability
     * @param p the estimated class probability
     * @return the LogitBoost response
     */
    protected double getZ(double actual, double p) {
	double z;
	if (actual == 1) {
	    z = 1.0 / p;
	    if (z > Z_MAX) { // threshold
		z = Z_MAX;
	    }
	} else {
	    z = -1.0 / (1.0 - p);
	    if (z < -Z_MAX) { // threshold
		z = -Z_MAX;
	    }
	}
	return z;
    }

    /**
     * Computes the LogitBoost response for an array of y/p values
     * (actual/estimated class probabilities).
     *
     * @param dataYs the actual class probabilities
     * @param probs the estimated class probabilities
     * @return the LogitBoost response
     */
    protected double[][] getZs(double[][] probs, double[][] dataYs) {

	double[][] dataZs = new double[probs.length][m_numClasses];
	for (int j = 0; j < m_numClasses; j++)
	    for (int i = 0; i < probs.length; i++) dataZs[i][j] = getZ(dataYs[i][j], probs[i][j]);
	return dataZs;
    }

    /**
     * Computes the LogitBoost weights from an array of y/p values
     * (actual/estimated class probabilities).
     *
     * @param dataYs the actual class probabilities
     * @param probs the estimated class probabilities
     * @return the LogitBoost weights
     */
    protected double[][] getWs(double[][] probs, double[][] dataYs) {

	double[][] dataWs = new double[probs.length][m_numClasses];
	for (int j = 0; j < m_numClasses; j++)
	    for (int i = 0; i < probs.length; i++){
	    double z = getZ(dataYs[i][j], probs[i][j]);
	    dataWs[i][j] = (dataYs[i][j] - probs[i][j]) / z;
	    }
	return dataWs;
    }

    /**
     * Computes the p-values (probabilities for the classes) from the F-values
     * of the logistic model.
     *
     * @param Fs the F-values
     * @return the p-values
     */
    protected double[] probs(double[] Fs) {

	double maxF = -Double.MAX_VALUE;
	for (int i = 0; i < Fs.length; i++) {
	    if (Fs[i] > maxF) {
		maxF = Fs[i];
	    }
	}
	double sum = 0;
	double[] probs = new double[Fs.length];
	for (int i = 0; i < Fs.length; i++) {
	    probs[i] = Math.exp(Fs[i] - maxF);
	    sum += probs[i];
	}

	Utils.normalize(probs, sum);
	return probs;
    }

    /**
     * Computes the Y-values (actual class probabilities) for a set of instances.
     *
     * @param data the data to compute the Y-values from
     * @return the Y-values
     */
    protected double[][] getYs(Instances data){

	double [][] dataYs = new double [data.numInstances()][m_numClasses];
	for (int j = 0; j < m_numClasses; j++) {
	    for (int k = 0; k < data.numInstances(); k++) {
		dataYs[k][j] = (data.instance(k).classValue() == j) ?
		    1.0: 0.0;
	    }
	}
	return dataYs;
    }

    /**
     * Computes the F-values for a single instance.
     *
     * @param instance the instance to compute the F-values for
     * @return the F-values
     * @throws Exception if something goes wrong
     */
    protected double[] getFs(Instance instance) throws Exception{

	double [] pred = new double [m_numClasses];
	double [] instanceFs = new double [m_numClasses];

	//add up the predictions from the simple regression functions
	for (int i = 0; i < m_numRegressions; i++) {
	    double predSum = 0;
	    for (int j = 0; j < m_numClasses; j++) {
		pred[j] = m_regressions[j][i].classifyInstance(instance);
		predSum += pred[j];
	    }
	    predSum /= m_numClasses;
	    for (int j = 0; j < m_numClasses; j++) {
		instanceFs[j] += (pred[j] - predSum) * (m_numClasses - 1)
		    / m_numClasses;
	    }
	}

	return instanceFs;
    }

    /**
     * Computes the F-values for a set of instances.
     *
     * @param data the data to work on
     * @return the F-values
     * @throws Exception if something goes wrong
     */
    protected double[][] getFs(Instances data) throws Exception{

	double[][] dataFs = new double[data.numInstances()][];

	for (int k = 0; k < data.numInstances(); k++) {
	    dataFs[k] = getFs(data.instance(k));
	}

	return dataFs;
    }

    /**
     * Computes the p-values (probabilities for the different classes) from
     * the F-values for a set of instances.
     *
     * @param dataFs the F-values
     * @return the p-values
     */
    protected double[][] getProbs(double[][] dataFs){

	int numInstances = dataFs.length;
	double[][] probs = new double[numInstances][];

	for (int k = 0; k < numInstances; k++) {
	    probs[k] = probs(dataFs[k]);
	}
	return probs;
    }

    /**
     * Returns the negative loglikelihood of the Y-values (actual class probabilities) given the
     * p-values (current probability estimates).
     *
     * @param dataYs the Y-values
     * @param probs the p-values
     * @return the likelihood
     */
    protected double negativeLogLikelihood(double[][] dataYs, double[][] probs) {

	double logLikelihood = 0;
	for (int i = 0; i < dataYs.length; i++) {
	    for (int j = 0; j < m_numClasses; j++) {
		if (dataYs[i][j] == 1.0) {
		    logLikelihood -= Math.log(probs[i][j]);
		}
	    }
	}
	return logLikelihood;// / (double)dataYs.length;
    }

    /**
     * Returns an array of the indices of the attributes used in the logistic model.
     * The first dimension is the class, the second dimension holds a list of attribute indices.
     * Attribute indices start at zero.
     * @return the array of attribute indices
     */
    public int[][] getUsedAttributes(){

	int[][] usedAttributes = new int[m_numClasses][];

	//first extract coefficients
	double[][] coefficients = getCoefficients();

	for (int j = 0; j < m_numClasses; j++){

	    //boolean array indicating if attribute used
	    boolean[] attributes = new boolean[m_numericDataHeader.numAttributes()];
	    for (int i = 0; i < attributes.length; i++) {
		//attribute used if coefficient > 0
		if (!Utils.eq(coefficients[j][i + 1],0)) attributes[i] = true;
	    }

	    int numAttributes = 0;
	    for (int i = 0; i < m_numericDataHeader.numAttributes(); i++) if (attributes[i]) numAttributes++;

	    //"collect" all attributes into array of indices
	    int[] usedAttributesClass = new int[numAttributes];
	    int count = 0;
	    for (int i = 0; i < m_numericDataHeader.numAttributes(); i++) {
		if (attributes[i]) {
		usedAttributesClass[count] = i;
		count++;
		}
	    }

	    usedAttributes[j] = usedAttributesClass;
	}

	return usedAttributes;
    }

    /**
     * The number of LogitBoost iterations performed (= the number of simple
     * regression functions fit).
     *
     * @return the number of LogitBoost iterations performed
     */
    public int getNumRegressions() {
	return m_numRegressions;
    }

    /**
     * Get the value of weightTrimBeta.
     *
     * @return Value of weightTrimBeta.
     */
    public double getWeightTrimBeta(){
        return m_weightTrimBeta;
    }

    /**
     * Get the value of useAIC.
     *
     * @return Value of useAIC.
     */
    public boolean getUseAIC(){
        return m_useAIC;
    }

    /**
     * Sets the parameter "maxIterations".
     *
     * @param maxIterations the maximum iterations
     */
    public void setMaxIterations(int maxIterations) {
	m_maxIterations = maxIterations;
    }

    /**
     * Sets the option "heuristicStop".
     *
     * @param heuristicStop the heuristic stop to use
     */
    public void setHeuristicStop(int heuristicStop){
	m_heuristicStop = heuristicStop;
    }

    /**
     * Sets the option "weightTrimBeta".
     */
    public void setWeightTrimBeta(double w){
        m_weightTrimBeta = w;
    }

    /**
     * Set the value of useAIC.
     *
     * @param c Value to assign to useAIC.
     */
    public void setUseAIC(boolean c){
        m_useAIC = c;
    }

    /**
     * Returns the maxIterations parameter.
     *
     * @return the maximum iteration
     */
    public int getMaxIterations(){
	return m_maxIterations;
    }

    /**
     * Returns an array holding the coefficients of the logistic model.
     * First dimension is the class, the second one holds a list of coefficients.
     * At position zero, the constant term of the model is stored, then, the coefficients for
     * the attributes in ascending order.
     * @return the array of coefficients
     */
    protected double[][] getCoefficients(){
	double[][] coefficients = new double[m_numClasses][m_numericDataHeader.numAttributes() + 1];
	for (int j = 0; j < m_numClasses; j++) {
	    //go through simple regression functions and add their coefficient to the coefficient of
	    //the attribute they are built on.
	    for (int i = 0; i < m_numRegressions; i++) {

		double slope = m_regressions[j][i].getSlope();
		double intercept = m_regressions[j][i].getIntercept();
		int attribute = m_regressions[j][i].getAttributeIndex();

		coefficients[j][0] += intercept;
		coefficients[j][attribute + 1] += slope;
	    }
	}

        // Need to multiply all coefficients by (J-1) / J
        for (int j = 0; j < coefficients.length; j++) {
          for (int i = 0; i < coefficients[0].length; i++) {
            coefficients[j][i] *= (double)(m_numClasses - 1) / (double)m_numClasses;
          }
        }

	return coefficients;
    }

    /**
     * Returns the fraction of all attributes in the data that are used in the
     * logistic model (in percent).
     * An attribute is used in the model if it is used in any of the models for
     * the different classes.
     *
     * @return the fraction of all attributes that are used
     */
    public double percentAttributesUsed(){
	boolean[] attributes = new boolean[m_numericDataHeader.numAttributes()];

	double[][] coefficients = getCoefficients();
	for (int j = 0; j < m_numClasses; j++){
	    for (int i = 1; i < m_numericDataHeader.numAttributes() + 1; i++) {
		//attribute used if it is used in any class, note coefficients are shifted by one (because
		//of constant term).
		if (!Utils.eq(coefficients[j][i],0)) attributes[i - 1] = true;
	    }
	}

	//count number of used attributes (without the class attribute)
	double count = 0;
	for (int i = 0; i < attributes.length; i++) if (attributes[i]) count++;
	return count / (double)(m_numericDataHeader.numAttributes() - 1) * 100.0;
    }

    /**
     * Returns a description of the logistic model (i.e., attributes and
     * coefficients).
     *
     * @return the description of the model
     */
    public String toString(){

	StringBuffer s = new StringBuffer();

	//get used attributes
	int[][] attributes = getUsedAttributes();

	//get coefficients
	double[][] coefficients = getCoefficients();

	for (int j = 0; j < m_numClasses; j++) {
	    s.append("\nClass "+j+" :\n");
	    //constant term
	    s.append(Utils.doubleToString(coefficients[j][0],4,2)+" + \n");
	    for (int i = 0; i < attributes[j].length; i++) {
		//attribute/coefficient pairs
		s.append(Utils.doubleToString(coefficients[j][attributes[j][i]+1],4,2) + "*");
                s.append("["+m_numericDataHeader.attribute(attributes[j][i]).name()+"]");
		if (i != attributes[j].length - 1) s.append(" +");
		s.append("\n");
	    }
	}
	return new String(s);
    }

    /**
     * Returns class probabilities for an instance.
     *
     * @param instance the instance to compute the distribution for
     * @return the class probabilities
     * @throws Exception if distribution can't be computed successfully
     */
    public double[] distributionForInstance(Instance instance) throws Exception {

	instance = (Instance)instance.copy();

	//set to numeric pseudo-class
      	instance.setDataset(m_numericDataHeader);

	//calculate probs via Fs
	return probs(getFs(instance));
    }

    /**
     * Cleanup in order to save memory.
     */
    public void cleanup() {
	//save just header info
	m_train = new Instances(m_train,0);
	m_numericData = null;
    }

    /**
     * Returns the revision string.
     *
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.9 $");
    }
}


